Rare category detection using hierarchical mean shift PublicDeposited

Descriptions

Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a more difficult task is to identify anomalies that are both interesting and statistically significant. Category detection is an emerging area of machine learning that can address this issue using a "human-in-the-loop" approach. In this interactive setting, the algorithm asks the user to label a query data point under an existing category or declare the query data point to belong to a previously undiscovered category. The goal of category detection is to discover all the categories in the data in as few queries as possible. In a data set with imbalanced categories, the main challenge is in identifying the rare categories or anomalies; hence, the task is often referred to as rare category detection.
We present a new approach to rare category detection using a hierarchical mean shift procedure. In our approach, a hierarchy is created by repeatedly applying mean shift with increasing bandwidth on the entire data set. This hierarchy allows us to identify anomalies in the data set at different scales, which are then posed as queries to the user. The main advantage of this methodology over existing approaches is that it does not require any knowledge of the data set properties such as the total number of classes or the prior probabilities of the classes. Results on real-world data sets show that our hierarchical mean shift approach performs consistently better than previous techniques.